Overview

Dataset statistics

Number of variables35
Number of observations66806
Missing cells0
Missing cells (%)0.0%
Duplicate rows111
Duplicate rows (%)0.2%
Total size in memory11.5 MiB
Average record size in memory180.0 B

Variable types

Categorical26
Numeric9

Alerts

company_null_flag has constant value "0"Constant
Dataset has 111 (0.2%) duplicate rowsDuplicates
consumer_complaint_narrative has a high cardinality: 65646 distinct valuesHigh cardinality
label is highly overall correlated with product and 6 other fieldsHigh correlation
sub_product_freq is highly overall correlated with product and 1 other fieldsHigh correlation
issue_freq is highly overall correlated with sub_issue_freq and 4 other fieldsHigh correlation
sub_issue_freq is highly overall correlated with issue_freq and 2 other fieldsHigh correlation
company_public_response_freq is highly overall correlated with company_public_response_null_flag and 1 other fieldsHigh correlation
company_freq is highly overall correlated with sub_product_null_flag and 2 other fieldsHigh correlation
state_freq is highly overall correlated with state_null_flag and 1 other fieldsHigh correlation
zipcode_freq is highly overall correlated with state_null_flag and 1 other fieldsHigh correlation
product is highly overall correlated with label and 9 other fieldsHigh correlation
sub_product_null_flag is highly overall correlated with label and 4 other fieldsHigh correlation
sub_product_low_flag is highly overall correlated with label and 1 other fieldsHigh correlation
issue_low_flag is highly overall correlated with label and 2 other fieldsHigh correlation
sub_issue_null_flag is highly overall correlated with label and 4 other fieldsHigh correlation
sub_issue_low_flag is highly overall correlated with label and 1 other fieldsHigh correlation
company_public_response_null_flag is highly overall correlated with company_public_response_freqHigh correlation
company_public_response_low_flag is highly overall correlated with company_public_response_freqHigh correlation
company_low_flag is highly overall correlated with label and 2 other fieldsHigh correlation
state_null_flag is highly overall correlated with state_freq and 2 other fieldsHigh correlation
zipcode_null_flag is highly overall correlated with state_freq and 2 other fieldsHigh correlation
Not Older American, Not Servicemember is highly overall correlated with Older American and 1 other fieldsHigh correlation
Older American is highly overall correlated with Not Older American, Not ServicememberHigh correlation
Servicemember is highly overall correlated with Not Older American, Not ServicememberHigh correlation
Closed with explanation is highly overall correlated with Closed with monetary relief and 1 other fieldsHigh correlation
Closed with monetary relief is highly overall correlated with Closed with explanationHigh correlation
Closed with non-monetary relief is highly overall correlated with Closed with explanationHigh correlation
timely_response is highly imbalanced (79.3%)Imbalance
sub_product_low_flag is highly imbalanced (65.1%)Imbalance
company_public_response_low_flag is highly imbalanced (77.0%)Imbalance
state_null_flag is highly imbalanced (97.2%)Imbalance
state_low_flag is highly imbalanced (55.9%)Imbalance
zipcode_null_flag is highly imbalanced (97.2%)Imbalance
Older American is highly imbalanced (56.0%)Imbalance
Older American, Servicemember is highly imbalanced (90.4%)Imbalance
Servicemember is highly imbalanced (64.4%)Imbalance
Closed is highly imbalanced (82.4%)Imbalance
Closed with monetary relief is highly imbalanced (61.8%)Imbalance
Untimely response is highly imbalanced (93.3%)Imbalance
consumer_complaint_narrative is uniformly distributedUniform
label has 5711 (8.5%) zerosZeros
days_between_receipt_and_sent has 48903 (73.2%) zerosZeros

Reproduction

Analysis started2023-01-03 16:39:36.371302
Analysis finished2023-01-03 16:41:23.100916
Duration1 minute and 46.73 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

product
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
Debt collection
17552 
Mortgage
14919 
Credit reporting
12526 
Credit card
7929 
Bank account or service
5711 
Other values (6)
8169 

Length

Max length23
Median length15
Mean length13.558782
Min length8

Characters and Unicode

Total characters905808
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDebt collection
2nd rowConsumer Loan
3rd rowMortgage
4th rowMortgage
5th rowMortgage

Common Values

ValueCountFrequency (%)
Debt collection 17552
26.3%
Mortgage 14919
22.3%
Credit reporting 12526
18.7%
Credit card 7929
11.9%
Bank account or service 5711
 
8.5%
Consumer Loan 3678
 
5.5%
Student loan 2128
 
3.2%
Prepaid card 861
 
1.3%
Payday loan 726
 
1.1%
Money transfers 666
 
1.0%

Length

2023-01-03T10:41:23.323233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
credit 20455
15.7%
debt 17552
13.5%
collection 17552
13.5%
mortgage 14919
11.5%
reporting 12526
9.6%
card 8790
6.7%
loan 6532
 
5.0%
service 5821
 
4.5%
or 5711
 
4.4%
account 5711
 
4.4%
Other values (9) 14656
11.3%

Most occurring characters

ValueCountFrequency (%)
e 102755
11.3%
t 93747
10.3%
r 86729
9.6%
o 84847
 
9.4%
63419
 
7.0%
c 61247
 
6.8%
i 57435
 
6.3%
n 55390
 
6.1%
a 44862
 
5.0%
g 42364
 
4.7%
Other values (20) 213013
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 771905
85.2%
Uppercase Letter 70484
 
7.8%
Space Separator 63419
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 102755
13.3%
t 93747
12.1%
r 86729
11.2%
o 84847
11.0%
c 61247
7.9%
i 57435
7.4%
n 55390
7.2%
a 44862
5.8%
g 42364
5.5%
l 38068
 
4.9%
Other values (11) 104461
13.5%
Uppercase Letter
ValueCountFrequency (%)
C 24133
34.2%
D 17552
24.9%
M 15585
22.1%
B 5711
 
8.1%
L 3678
 
5.2%
S 2128
 
3.0%
P 1587
 
2.3%
O 110
 
0.2%
Space Separator
ValueCountFrequency (%)
63419
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 842389
93.0%
Common 63419
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 102755
12.2%
t 93747
11.1%
r 86729
10.3%
o 84847
10.1%
c 61247
 
7.3%
i 57435
 
6.8%
n 55390
 
6.6%
a 44862
 
5.3%
g 42364
 
5.0%
l 38068
 
4.5%
Other values (19) 174945
20.8%
Common
ValueCountFrequency (%)
63419
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 905808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 102755
11.3%
t 93747
10.3%
r 86729
9.6%
o 84847
 
9.4%
63419
 
7.0%
c 61247
 
6.8%
i 57435
 
6.3%
n 55390
 
6.1%
a 44862
 
5.0%
g 42364
 
4.7%
Other values (20) 213013
23.5%

consumer_complaint_narrative
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct65646
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
This company continues to report on my credit report after I sent them a letter telling them that this account was not mine and I have no idea what it is or who it belongs to! I asked for proof of a signed contract, I asked for a license to collect in my state, I asked for copies of all information referenced for this debt and still to date, I have not received anything but harassment from this company! THIS IS NOT MY DEBT! I WANT THIS ACCOUNT REMOVED FROM MY CREDIT REPORT AND THIS COMPANY TO STOP CONTACTING ME IMMEDIATELY!
 
37
I have been a victim of Identity Theft. I have been trying to work with the Credit Reporting Agency but they are refusing to honor my valid identity theft case thus these incorrect/fraudulent items are still on my credit report and they must be removed immediately but they are do not belong to me. I have provided all of the proof to show that I was a victim of Identity Theft and that to the best of my knowledge these fraudulent accounts do not belong to me. Please take immediate action on my behalf so I can have these items removed, deleted and permanently blocked from my credit report, so that I can get back on track to a normal life. Regards
 
27
I have sent several requests to Experian requesting an investigation of my accounts. It has been months and I still have not received a response to my concerns. The only thing I have received is some automated rejection letter stating that they wo n't do anything to help me ( please see attached ). This has to be a violation of my rights. I feel like I 've already wasted so much time just trying to get Experian to look at the errors on my credit report and I 'm so frustrated that Experian is intentionally not responding to my inquiries. I need to have this issue resolved immediately. There are many things I need to do with my life and they all involve my credit. But I am not able to move forward all because of this credit bureau!
 
26
This company continues to report on my credit report after I sent them a letter telling them that this account was not mine and I have no idea what it is or who it belongs to! I asked for proof of a signed contract, I asked for a license to collect in my state, I asked for copies of all information referenced for this debt and still to date, I have not received anything but harassment from this company! THIS IS NOT MY DEBT!
 
24
I am filing this complaint because I think what Experian is doing to me is wrong, unethical and may be against the law. A letter was mailed to Experian on or around [ XXXX/XXXX/15 ]. I clearly stated my concerns to them regarding inaccurate, questionable or unverifiable information listed on my credit report. I also provided a clear copy of my ID, proof of my social security number and a proof of my current mailing address. On [ XXXX/XXXX/15 ] I received a notice from Experian stating the following : '' We received a suspicious request in the mail regarding your personal credit report and determined that it was not sent by you. Suspicious requests are reviewed by Experian security personnel who work regularly with law enforcement officials and regulatory agencies to identify fraudulent and deceptive correspondence purporting to originate from consumers. In an effort to safeguard your personal credit information from fraud, we will not be initiating any disputes based on the suspicious correspondence. Experian will apply this same policy to any future suspicious requests that we receive regarding your personal credit information, but we will not send additional notices to you of suspicious correspondence. If you believe that information in your personal credit report is inaccurate or incomplete, please visit our website at experian.com/validate dispute or call us at XXXX ( XXXX ) XXXX to speak directly to an Experian consumer assistance representative. " However, an Experian confirmation number was not even provided on this notice. I can not imagine what could have possibly been " suspicious '' about my correspondence to Experian. I should not have to spend endless hours on phone calling the credit bureau back and forth attempting to resolve the issues on my credit report! I am filing this complaint because Experian will not respond to any letters that I am sending in. I 'm sure this could be a violation of my rights and Experian should not be deliberately disregarding my concerns!
 
14
Other values (65641)
66678 

Length

Max length5153
Median length3311
Mean length1039.5873
Min length10

Characters and Unicode

Total characters69450671
Distinct characters100
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64938 ?
Unique (%)97.2%

Sample

1st rowXXXX has claimed I owe them {$27.00} for XXXX years despite the PROOF of PAYMENT I sent them : canceled check and their ownPAID INVOICE for {$27.00}! They continue to insist I owe them and collection agencies are after me. How can I stop this harassment for a bill I already paid four years ago?
2nd rowDue to inconsistencies in the amount owed that I was told by M & T Bank and the amount that was reported to the credit reporting agencies, I was advised to write a good will letter in order to address the issue and request the negative entry be removed from my credit report all together. I had a vehicle that was stolen and it was declared a total loss by insurance company. The insurance company and the GAP insurancw companypaid the outstanding balance of the loan, but I was told by M & T Bank that there was still a balance due on the loan. In good faith, without having received any proof as to why there was still a balance, I made a partial payment towards the remaining debt. I then sent the goodwill letter still offering to pay the remainder of the debt, but in exchange for the removal of the negative entry on my credit report. At one point, in XXXX 2015, per my credit monitoring agency, it showed a delinquent balance of {$0.00}, but when I checked my credit report again on XXXX XXXX 2015, there was a delinquent balance of {$1400.00}. The monies from the GAP insurance and my insurance company has been paid, M & T Bank says that I still owe {$620.00}, of which {$210.00} has already been paid. I contacted M & T Bank via return receipt mail, but I have not been given the courtesy of a response yet.
3rd rowIn XX/XX/XXXX my wages that I earned at my job decreased by almost half, by XX/XX/XXXX I knew I was in trouble with my home loan. I began contacting WFB whom my home loan is with, for assitance and options. In early XX/XX/XXXX I began the Loan Modification process with Wells Fargo Bank. I was told that they would not assist me with anything financial on my home loan until I fell 90 days behind, though at the time I started to inquire for assistance from WFB I was only a few weeks behind. So, I began working with a program called XXXX. They approved me for a variety of assistence and reached out to Wells Fargo Bank to determine what they could assist with. Wells Fargo then turned down the assistance from XXXX and finally offered to do a Loan Modification for me. The outcome was totally unknow about what I would be offered in the end by WFB for assistance. Wells Fargo lost my paperwork twice during this process, so it took 2 months from the time I started to the time my paperwork began to be processed for some kind of approval. In XX/XX/XXXX I was in a trial period of 3 months of payments based on a slightly modified amount. Roughly {$75.00} less than what I was paying on my original payment. However, my caseworker with WFB, failed to tell me that since the payments during this time were not the full amount of the original mortgage payment so they were not applied to the loan. I discovered this in XX/XX/XXXX when I was told by a creditor with XXXX that she could n't restore my line of credit because of something to do with my mortgage. I then called and found this out. My caseworker at WFB then told me it was n't her responsibility to tell me this information. I told her that she could have told me that if I could come up with the remaining amount of {$75.00} then the payments would be applied. Instead, she chose to withhold this information and damage my credit even further. Now we are into 5 months ( including the 90 days WFB told me I had to fall behind until they would help me ) without an applied payment to my home loan, going on 6 months. More paperwork was lost in XXXX as the modification was being finalized and my loan modification did n't go through until XX/XX/XXXX. I spoke to a lawyer that is working on a class-action lawsuit against WFB for people in a similar situation and said this paper loss is typical. Wells Fargo did not reduce the interest rate, but they did reduce the payment by less than {$100.00} and the payments I was behind went back into the loan. My property taxes also fell behind in my escrow account at this time because there was no payment on the loan. So, I am behind on my property taxes, have damaged credit, and and I am not in any better payment situation than I was. They can not give me a reason on why they rejected XXXX assistance. I was originally told they were not an approved agency but on a later phone call to Wells Fargo Bank I was told that they work with that program often and the WFB employee could not understand why they would reject the help. I also spoke with XXXX and they said that Wells Fargo works with this program all the time and could n't understand why they would do this. I feel that I have been taken advantage of by Wells Fargo and would like to pursue legal action against them based on negligence. Although my payments are back on track I am not making much more money now than I was before so it is only a matter of time before something like this happens again if something unforeseen happens to my life that affects my finances.
4th rowI have an open and current mortgage with Chase Bank # XXXX. Chase is reporting the loan payments to XXXX but XXXX is surpressing the information and reporting the loan as Discharged in BK. This mortgage was reaffirmed in a Chapter XXXX BK discharged dated XXXX/XXXX/2013. Chase keeps referring to BK Law for Chapter XXXX and we keep providing documentation for Chapter XXXX, and the account should be open and current with all the payments
5th rowXXXX was submitted XX/XX/XXXX. At the time I submitted this complaint, I had dealt with Rushmore Mortgage directly endeavoring to get them to stop the continuous daily calls I was receiving trying to collect on a mortgage for which I was not responsible due to bankruptcy. They denied having knowledge of the bankruptcy, even though I had spoken with them about it repeatedly and had written them repeatedly referencing the bankruptcy requesting them to cease the pursuit, they continued to do so. When they were unable to trick me into paying, force me into paying in retaliation they placed reported to my credit bureaus a past due mortgage amount that had been discharged in Federal Court. On XX/XX/XXXX Rushmore responded the referenced complaint indicating that they would remove the reporting from my bureau, yet it is still there now in XX/XX/XXXX. I would like them to remove it immediately and send me a letter indicating that it should not have been there in the first place and they are going to remove it from all my bureaus. Rushmore, when speaking to me, represented themselves as the new note holder, but when CFPB was involved, they identified themselves as the servicing agency for XXXX XXXX XXXX. This credit bullying and racial discrimination practices is damaging to anyone who is exposed to these tactics and this needs to stop. Them denying their intent and then walking away with no penalties of any kind is one of the reasons it continues. Please assist me in procuring the resolution once and for all.

Common Values

ValueCountFrequency (%)
This company continues to report on my credit report after I sent them a letter telling them that this account was not mine and I have no idea what it is or who it belongs to! I asked for proof of a signed contract, I asked for a license to collect in my state, I asked for copies of all information referenced for this debt and still to date, I have not received anything but harassment from this company! THIS IS NOT MY DEBT! I WANT THIS ACCOUNT REMOVED FROM MY CREDIT REPORT AND THIS COMPANY TO STOP CONTACTING ME IMMEDIATELY! 37
 
0.1%
I have been a victim of Identity Theft. I have been trying to work with the Credit Reporting Agency but they are refusing to honor my valid identity theft case thus these incorrect/fraudulent items are still on my credit report and they must be removed immediately but they are do not belong to me. I have provided all of the proof to show that I was a victim of Identity Theft and that to the best of my knowledge these fraudulent accounts do not belong to me. Please take immediate action on my behalf so I can have these items removed, deleted and permanently blocked from my credit report, so that I can get back on track to a normal life. Regards 27
 
< 0.1%
I have sent several requests to Experian requesting an investigation of my accounts. It has been months and I still have not received a response to my concerns. The only thing I have received is some automated rejection letter stating that they wo n't do anything to help me ( please see attached ). This has to be a violation of my rights. I feel like I 've already wasted so much time just trying to get Experian to look at the errors on my credit report and I 'm so frustrated that Experian is intentionally not responding to my inquiries. I need to have this issue resolved immediately. There are many things I need to do with my life and they all involve my credit. But I am not able to move forward all because of this credit bureau! 26
 
< 0.1%
This company continues to report on my credit report after I sent them a letter telling them that this account was not mine and I have no idea what it is or who it belongs to! I asked for proof of a signed contract, I asked for a license to collect in my state, I asked for copies of all information referenced for this debt and still to date, I have not received anything but harassment from this company! THIS IS NOT MY DEBT! 24
 
< 0.1%
I am filing this complaint because I think what Experian is doing to me is wrong, unethical and may be against the law. A letter was mailed to Experian on or around [ XXXX/XXXX/15 ]. I clearly stated my concerns to them regarding inaccurate, questionable or unverifiable information listed on my credit report. I also provided a clear copy of my ID, proof of my social security number and a proof of my current mailing address. On [ XXXX/XXXX/15 ] I received a notice from Experian stating the following : '' We received a suspicious request in the mail regarding your personal credit report and determined that it was not sent by you. Suspicious requests are reviewed by Experian security personnel who work regularly with law enforcement officials and regulatory agencies to identify fraudulent and deceptive correspondence purporting to originate from consumers. In an effort to safeguard your personal credit information from fraud, we will not be initiating any disputes based on the suspicious correspondence. Experian will apply this same policy to any future suspicious requests that we receive regarding your personal credit information, but we will not send additional notices to you of suspicious correspondence. If you believe that information in your personal credit report is inaccurate or incomplete, please visit our website at experian.com/validate dispute or call us at XXXX ( XXXX ) XXXX to speak directly to an Experian consumer assistance representative. " However, an Experian confirmation number was not even provided on this notice. I can not imagine what could have possibly been " suspicious '' about my correspondence to Experian. I should not have to spend endless hours on phone calling the credit bureau back and forth attempting to resolve the issues on my credit report! I am filing this complaint because Experian will not respond to any letters that I am sending in. I 'm sure this could be a violation of my rights and Experian should not be deliberately disregarding my concerns! 14
 
< 0.1%
This company continues to report on my credit report after I sent them a letter telling them that this account was not mine and I have no idea what it is or who it belongs to! I asked for proof of a signed contract, I asked for a license to collect in my state, I asked for copies of all information referenced for this debt and still to date, I have not received anything but harassment from this company! 14
 
< 0.1%
While checking my personal credit report, I noticed an unauthorized and fraudulent credit inquiry made by XXXX on or about XX/XX/XXXX on Transunion. I did not authorized anyone employed by this company to make any inquiry and view my credit report. XXXX has violated the Fair Credit Reporting Act Section 1681b ( c ). They were not legally entitled to make this fraudulent inquiry. This is a serious breach of my privacy rights. I have requested that they mail me a copy of my signed authorization form that gave them the right to view my credit within five ( 5 ) business days so that I can verify its validity and advised them that if they can not provide me with proof that I authorized them to view my credit report then I am demanding that they contact the credit bureaus immediately and have them remove the unauthorized and fraudulent hard inquiry immediately. I also requested that they remove my personal information from their records. My Social Security # is XXXX and my Date of Birth is XX/XX/XXXX in case it is needed to locate the fraudulent inquiry in their system. 14
 
< 0.1%
In XXXX, I requested my free annual credit report. After viewing my credit report, I noticed over XXXX credit/loan inquiries on my account that was not authorized by me. I contacted each company and was told that there was not any account open under my information. I explained that I have a hard inquiry on my credit report and need it removed as soon as possible so that I could apply for a home. I was told that the inquiries would be removed from my credit report. As of XXXX/XXXX/XXXX, none of the inquiries have been removed dating back to XXXX. I had a initial fraud alert placed on my report on XXXX XXXX, so if any credit reports are being pulled with my information, I was to be contacted by the company before any action was taken place. I just want the inquiries to be removed so that my scores and credit is not being impacted any longer. I would like to purchase a house, but until all of the inquires are removed I will not be approved with XXXX inquires on my credit report. 12
 
< 0.1%
While checking my personal credit report, I noticed an unauthorized and fraudulent credit inquiry made by XXXX on or about XX/XX/XXXX on Experian. I did not authorized anyone employed by this company to make any inquiry and view my credit report. XXXX has violated the Fair Credit Reporting Act Section 1681b ( c ). They were not legally entitled to make this fraudulent inquiry. This is a serious breach of my privacy rights. I have requested that they mail me a copy of my signed authorization form that gave them the right to view my credit within five ( 5 ) business days so that I can verify its validity and advised them that if they can not provide me with proof that I authorized them to view my credit report then I am demanding that they contact the credit bureaus immediately and have them remove the unauthorized and fraudulent hard inquiry immediately. I also requested that they remove my personal information from their records. My Social Security # is XXXX and my Date of Birth is XX/XX/XXXX in case it is needed to locate the fraudulent inquiry in their system. 11
 
< 0.1%
I have been a victim of Identity Theft, and I am trying to dispute inaccurate items on my credit report. 10
 
< 0.1%
Other values (65636) 66617
99.7%

Length

2023-01-03T10:41:24.087728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xxxx 542454
 
4.3%
the 524959
 
4.1%
i 477279
 
3.7%
to 436494
 
3.4%
and 346105
 
2.7%
a 261506
 
2.1%
my 255735
 
2.0%
239141
 
1.9%
of 200894
 
1.6%
that 198782
 
1.6%
Other values (71890) 9252815
72.6%

Most occurring characters

ValueCountFrequency (%)
12737421
18.3%
e 6284731
 
9.0%
t 4926643
 
7.1%
a 4289438
 
6.2%
o 3747058
 
5.4%
n 3648223
 
5.3%
i 3156306
 
4.5%
r 2818894
 
4.1%
X 2685315
 
3.9%
s 2668360
 
3.8%
Other values (90) 22488282
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49245170
70.9%
Space Separator 12737421
 
18.3%
Uppercase Letter 5023578
 
7.2%
Other Punctuation 1417831
 
2.0%
Decimal Number 543978
 
0.8%
Control 159501
 
0.2%
Close Punctuation 107428
 
0.2%
Open Punctuation 104506
 
0.2%
Currency Symbol 63488
 
0.1%
Dash Punctuation 44397
 
0.1%
Other values (3) 3373
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6284731
12.8%
t 4926643
 
10.0%
a 4289438
 
8.7%
o 3747058
 
7.6%
n 3648223
 
7.4%
i 3156306
 
6.4%
r 2818894
 
5.7%
s 2668360
 
5.4%
h 2341861
 
4.8%
d 2251202
 
4.6%
Other values (17) 13112454
26.6%
Uppercase Letter
ValueCountFrequency (%)
X 2685315
53.5%
I 570466
 
11.4%
T 229426
 
4.6%
A 176019
 
3.5%
E 137509
 
2.7%
C 130445
 
2.6%
S 129876
 
2.6%
O 108837
 
2.2%
N 101917
 
2.0%
R 81572
 
1.6%
Other values (16) 672196
 
13.4%
Other Punctuation
ValueCountFrequency (%)
. 695388
49.0%
, 342471
24.2%
' 136759
 
9.6%
/ 122665
 
8.7%
" 29617
 
2.1%
! 20825
 
1.5%
: 16866
 
1.2%
? 14994
 
1.1%
; 9907
 
0.7%
# 8239
 
0.6%
Other values (5) 20100
 
1.4%
Decimal Number
ValueCountFrequency (%)
0 264514
48.6%
1 73810
 
13.6%
2 59864
 
11.0%
5 48733
 
9.0%
3 26538
 
4.9%
4 18840
 
3.5%
6 17252
 
3.2%
9 11633
 
2.1%
7 11536
 
2.1%
8 11258
 
2.1%
Math Symbol
ValueCountFrequency (%)
+ 985
46.9%
= 687
32.7%
~ 218
 
10.4%
| 146
 
7.0%
> 38
 
1.8%
< 24
 
1.1%
Control
ValueCountFrequency (%)
159480
> 99.9%
18
 
< 0.1%
€ 3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
{ 58474
56.0%
( 45146
43.2%
[ 886
 
0.8%
Close Punctuation
ValueCountFrequency (%)
} 58472
54.4%
) 48079
44.8%
] 877
 
0.8%
Currency Symbol
ValueCountFrequency (%)
$ 63485
> 99.9%
¢ 3
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
` 98
97.0%
^ 3
 
3.0%
Space Separator
ValueCountFrequency (%)
12737421
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44397
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54268748
78.1%
Common 15181923
 
21.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6284731
 
11.6%
t 4926643
 
9.1%
a 4289438
 
7.9%
o 3747058
 
6.9%
n 3648223
 
6.7%
i 3156306
 
5.8%
r 2818894
 
5.2%
X 2685315
 
4.9%
s 2668360
 
4.9%
h 2341861
 
4.3%
Other values (43) 17701919
32.6%
Common
ValueCountFrequency (%)
12737421
83.9%
. 695388
 
4.6%
, 342471
 
2.3%
0 264514
 
1.7%
159480
 
1.1%
' 136759
 
0.9%
/ 122665
 
0.8%
1 73810
 
0.5%
$ 63485
 
0.4%
2 59864
 
0.4%
Other values (37) 526066
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69450662
> 99.9%
None 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12737421
18.3%
e 6284731
 
9.0%
t 4926643
 
7.1%
a 4289438
 
6.2%
o 3747058
 
5.4%
n 3648223
 
5.3%
i 3156306
 
4.5%
r 2818894
 
4.1%
X 2685315
 
3.9%
s 2668360
 
3.8%
Other values (87) 22488273
32.4%
None
ValueCountFrequency (%)
â 3
33.3%
€ 3
33.3%
¢ 3
33.3%

timely_response
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
1
64638 
0
 
2168

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 64638
96.8%
0 2168
 
3.2%

Length

2023-01-03T10:41:24.494990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:24.837044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 64638
96.8%
0 2168
 
3.2%

Most occurring characters

ValueCountFrequency (%)
1 64638
96.8%
0 2168
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 64638
96.8%
0 2168
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 64638
96.8%
0 2168
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 64638
96.8%
0 2168
 
3.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
51229 
1
15577 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 51229
76.7%
1 15577
 
23.3%

Length

2023-01-03T10:41:25.152798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:25.489850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51229
76.7%
1 15577
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 51229
76.7%
1 15577
 
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51229
76.7%
1 15577
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51229
76.7%
1 15577
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51229
76.7%
1 15577
 
23.3%

label
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8285932
Minimum0
Maximum10
Zeros5711
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size261.1 KiB
2023-01-03T10:41:25.750277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2487952
Coefficient of variation (CV)0.58736854
Kurtosis0.40879874
Mean3.8285932
Median Absolute Deviation (MAD)2
Skewness0.48705234
Sum255773
Variance5.0570799
MonotonicityNot monotonic
2023-01-03T10:41:26.052108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 17552
26.3%
6 14919
22.3%
3 12526
18.7%
2 7929
11.9%
0 5711
 
8.5%
1 3678
 
5.5%
10 2128
 
3.2%
9 861
 
1.3%
8 726
 
1.1%
5 666
 
1.0%
ValueCountFrequency (%)
0 5711
 
8.5%
1 3678
 
5.5%
2 7929
11.9%
3 12526
18.7%
4 17552
26.3%
5 666
 
1.0%
6 14919
22.3%
7 110
 
0.2%
8 726
 
1.1%
9 861
 
1.3%
ValueCountFrequency (%)
10 2128
 
3.2%
9 861
 
1.3%
8 726
 
1.1%
7 110
 
0.2%
6 14919
22.3%
5 666
 
1.0%
4 17552
26.3%
3 12526
18.7%
2 7929
11.9%
1 3678
 
5.5%
Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9272969
Minimum0
Maximum290
Zeros48903
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size522.0 KiB
2023-01-03T10:41:26.441612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9
Maximum290
Range290
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.0190525
Coefficient of variation (CV)3.123054
Kurtosis188.31572
Mean1.9272969
Median Absolute Deviation (MAD)0
Skewness8.8965712
Sum128755
Variance36.228993
MonotonicityNot monotonic
2023-01-03T10:41:26.849289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 48903
73.2%
2 2707
 
4.1%
1 2413
 
3.6%
3 2128
 
3.2%
5 2082
 
3.1%
4 2073
 
3.1%
6 1512
 
2.3%
7 1032
 
1.5%
8 493
 
0.7%
9 314
 
0.5%
Other values (89) 3149
 
4.7%
ValueCountFrequency (%)
0 48903
73.2%
1 2413
 
3.6%
2 2707
 
4.1%
3 2128
 
3.2%
4 2073
 
3.1%
5 2082
 
3.1%
6 1512
 
2.3%
7 1032
 
1.5%
8 493
 
0.7%
9 314
 
0.5%
ValueCountFrequency (%)
290 1
< 0.1%
216 1
< 0.1%
189 1
< 0.1%
186 1
< 0.1%
163 1
< 0.1%
157 1
< 0.1%
134 1
< 0.1%
133 1
< 0.1%
106 1
< 0.1%
105 1
< 0.1%

sub_product_freq
Real number (ℝ)

Distinct43
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.27157484
Minimum-1
Maximum0.099377301
Zeros0
Zeros (%)0.0%
Negative20455
Negative (%)30.6%
Memory size522.0 KiB
2023-01-03T10:41:27.291380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0.030910397
Q30.056806275
95-th percentile0.099377301
Maximum0.099377301
Range1.0993773
Interquartile range (IQR)1.0568063

Descriptive statistics

Standard deviation0.48448611
Coefficient of variation (CV)-1.7839875
Kurtosis-1.2944189
Mean-0.27157484
Median Absolute Deviation (MAD)0.027362812
Skewness-0.83316928
Sum-18142.828
Variance0.23472679
MonotonicityNot monotonic
2023-01-03T10:41:27.679233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-1 20455
30.6%
0.09937730144 6639
 
9.9%
0.07863066192 5253
 
7.9%
0.05716552405 3819
 
5.7%
0.05680627489 3795
 
5.7%
0.05264497201 3517
 
5.3%
0.04155315391 2776
 
4.2%
0.04078974942 2725
 
4.1%
0.03676316499 2456
 
3.7%
0.03559560519 2378
 
3.6%
Other values (33) 12993
19.4%
ValueCountFrequency (%)
-1 20455
30.6%
1.496871538 × 10-51
 
< 0.1%
0.0001047810077 7
 
< 0.1%
0.0001496871538 20
 
< 0.1%
0.0001646558692 11
 
< 0.1%
0.0002095620154 28
 
< 0.1%
0.0002394994462 32
 
< 0.1%
0.0003442804539 23
 
< 0.1%
0.0004640301769 31
 
< 0.1%
0.0004789988923 32
 
< 0.1%
ValueCountFrequency (%)
0.09937730144 6639
9.9%
0.07863066192 5253
7.9%
0.05716552405 3819
5.7%
0.05680627489 3795
5.7%
0.05264497201 3517
5.3%
0.04155315391 2776
4.2%
0.04078974942 2725
4.1%
0.03676316499 2456
 
3.7%
0.03559560519 2378
 
3.6%
0.03091039727 2065
 
3.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
46351 
1
20455 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 46351
69.4%
1 20455
30.6%

Length

2023-01-03T10:41:28.024309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:28.363053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 46351
69.4%
1 20455
30.6%

Most occurring characters

ValueCountFrequency (%)
0 46351
69.4%
1 20455
30.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46351
69.4%
1 20455
30.6%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46351
69.4%
1 20455
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46351
69.4%
1 20455
30.6%

sub_product_low_flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
62429 
1
 
4377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62429
93.4%
1 4377
 
6.6%

Length

2023-01-03T10:41:28.641570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:29.006253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 62429
93.4%
1 4377
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 62429
93.4%
1 4377
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62429
93.4%
1 4377
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62429
93.4%
1 4377
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62429
93.4%
1 4377
 
6.6%

issue_freq
Real number (ℝ)

Distinct82
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.055581374
Minimum1.4968715 × 10-5
Maximum0.12501871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size522.0 KiB
2023-01-03T10:41:29.355200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.4968715 × 10-5
5-th percentile0.0039367721
Q10.015447714
median0.033350298
Q30.098104961
95-th percentile0.12501871
Maximum0.12501871
Range0.12500374
Interquartile range (IQR)0.082657246

Descriptive statistics

Standard deviation0.044977961
Coefficient of variation (CV)0.80922723
Kurtosis-1.5206067
Mean0.055581374
Median Absolute Deviation (MAD)0.027542436
Skewness0.38316484
Sum3713.1693
Variance0.002023017
MonotonicityNot monotonic
2023-01-03T10:41:29.822042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1250187109 8352
 
12.5%
0.1125647397 7520
 
11.3%
0.09810496063 6554
 
9.8%
0.07728347753 5163
 
7.7%
0.0435888992 2912
 
4.4%
0.04129868575 2759
 
4.1%
0.03335029788 2228
 
3.3%
0.02978774362 1990
 
3.0%
0.02469838038 1650
 
2.5%
0.02465347424 1647
 
2.5%
Other values (72) 26031
39.0%
ValueCountFrequency (%)
1.496871538 × 10-53
 
< 0.1%
5.987486154 × 10-58
 
< 0.1%
7.484357692 × 10-510
 
< 0.1%
8.981229231 × 10-56
 
< 0.1%
0.0001047810077 14
< 0.1%
0.0001197497231 8
 
< 0.1%
0.0001646558692 11
< 0.1%
0.0001796245846 12
< 0.1%
0.0001945933 26
< 0.1%
0.0002245307308 15
< 0.1%
ValueCountFrequency (%)
0.1250187109 8352
12.5%
0.1125647397 7520
11.3%
0.09810496063 6554
9.8%
0.07728347753 5163
7.7%
0.0435888992 2912
 
4.4%
0.04129868575 2759
 
4.1%
0.03335029788 2228
 
3.3%
0.02978774362 1990
 
3.0%
0.02469838038 1650
 
2.5%
0.02465347424 1647
 
2.5%

issue_low_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
55992 
1
10814 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 55992
83.8%
1 10814
 
16.2%

Length

2023-01-03T10:41:30.216512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:30.540331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 55992
83.8%
1 10814
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 55992
83.8%
1 10814
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55992
83.8%
1 10814
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55992
83.8%
1 10814
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55992
83.8%
1 10814
 
16.2%

sub_issue_freq
Real number (ℝ)

Distinct64
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.49521717
Minimum-1
Maximum0.068227405
Zeros0
Zeros (%)0.0%
Negative33874
Negative (%)50.7%
Memory size522.0 KiB
2023-01-03T10:41:30.857327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q30.017378679
95-th percentile0.068227405
Maximum0.068227405
Range1.0682274
Interquartile range (IQR)1.0173787

Descriptive statistics

Standard deviation0.51216771
Coefficient of variation (CV)-1.0342285
Kurtosis-1.9956592
Mean-0.49521717
Median Absolute Deviation (MAD)0
Skewness0.030727981
Sum-33083.479
Variance0.26231577
MonotonicityNot monotonic
2023-01-03T10:41:31.340727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 33874
50.7%
0.06822740472 4558
 
6.8%
0.03981678292 2660
 
4.0%
0.03376942191 2256
 
3.4%
0.01528305841 2042
 
3.1%
0.02933868215 1960
 
2.9%
0.02462353681 1645
 
2.5%
0.02359069545 1576
 
2.4%
0.01791755232 1197
 
1.8%
0.01737867856 1161
 
1.7%
Other values (54) 13877
20.8%
ValueCountFrequency (%)
-1 33874
50.7%
0.0003143430231 21
 
< 0.1%
0.0003293117385 22
 
< 0.1%
0.0004191240308 28
 
< 0.1%
0.0004340927462 29
 
< 0.1%
0.0004789988923 32
 
< 0.1%
0.0005837799 39
 
0.1%
0.0006586234769 44
 
0.1%
0.0007334670539 49
 
0.1%
0.0008083106308 54
 
0.1%
ValueCountFrequency (%)
0.06822740472 4558
6.8%
0.03981678292 2660
4.0%
0.03376942191 2256
3.4%
0.02933868215 1960
2.9%
0.02462353681 1645
 
2.5%
0.02359069545 1576
 
2.4%
0.01791755232 1197
 
1.8%
0.01737867856 1161
 
1.7%
0.01528305841 2042
3.1%
0.01179534772 788
 
1.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
1
33874 
0
32932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 33874
50.7%
0 32932
49.3%

Length

2023-01-03T10:41:31.758137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:32.109116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 33874
50.7%
0 32932
49.3%

Most occurring characters

ValueCountFrequency (%)
1 33874
50.7%
0 32932
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33874
50.7%
0 32932
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33874
50.7%
0 32932
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33874
50.7%
0 32932
49.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
55803 
1
11003 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 55803
83.5%
1 11003
 
16.5%

Length

2023-01-03T10:41:32.387727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:32.708822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 55803
83.5%
1 11003
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 55803
83.5%
1 11003
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55803
83.5%
1 11003
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 55803
83.5%
1 11003
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 55803
83.5%
1 11003
 
16.5%
Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.41269389
Minimum-1
Maximum0.28903093
Zeros0
Zeros (%)0.0%
Negative34030
Negative (%)50.9%
Memory size522.0 KiB
2023-01-03T10:41:32.961350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q30.28903093
95-th percentile0.28903093
Maximum0.28903093
Range1.2890309
Interquartile range (IQR)1.2890309

Descriptive statistics

Standard deviation0.60360978
Coefficient of variation (CV)-1.462609
Kurtosis-1.9336205
Mean-0.41269389
Median Absolute Deviation (MAD)0
Skewness0.086701112
Sum-27570.428
Variance0.36434477
MonotonicityNot monotonic
2023-01-03T10:41:33.288697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-1 34030
50.9%
0.2890309254 19309
28.9%
0.09832949136 6569
 
9.8%
0.05544412179 3704
 
5.5%
0.01037331976 693
 
1.0%
0.009894320869 661
 
1.0%
0.009475196839 633
 
0.9%
0.007334670539 490
 
0.7%
0.006870640362 459
 
0.7%
0.003682303985 246
 
0.4%
ValueCountFrequency (%)
-1 34030
50.9%
0.0001796245846 12
 
< 0.1%
0.003682303985 246
 
0.4%
0.006870640362 459
 
0.7%
0.007334670539 490
 
0.7%
0.009475196839 633
 
0.9%
0.009894320869 661
 
1.0%
0.01037331976 693
 
1.0%
0.05544412179 3704
 
5.5%
0.09832949136 6569
 
9.8%
ValueCountFrequency (%)
0.2890309254 19309
28.9%
0.09832949136 6569
 
9.8%
0.05544412179 3704
 
5.5%
0.01037331976 693
 
1.0%
0.009894320869 661
 
1.0%
0.009475196839 633
 
0.9%
0.007334670539 490
 
0.7%
0.006870640362 459
 
0.7%
0.003682303985 246
 
0.4%
0.0001796245846 12
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
1
34030 
0
32776 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 34030
50.9%
0 32776
49.1%

Length

2023-01-03T10:41:33.642664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:33.984938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 34030
50.9%
0 32776
49.1%

Most occurring characters

ValueCountFrequency (%)
1 34030
50.9%
0 32776
49.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 34030
50.9%
0 32776
49.1%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 34030
50.9%
0 32776
49.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 34030
50.9%
0 32776
49.1%

company_public_response_low_flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
64305 
1
 
2501

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 64305
96.3%
1 2501
 
3.7%

Length

2023-01-03T10:41:34.270796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:34.610327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 64305
96.3%
1 2501
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 64305
96.3%
1 2501
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64305
96.3%
1 2501
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64305
96.3%
1 2501
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64305
96.3%
1 2501
 
3.7%

company_freq
Real number (ℝ)

Distinct158
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023060017
Minimum1.4968715 × 10-5
Maximum0.062793761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size522.0 KiB
2023-01-03T10:41:34.987044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.4968715 × 10-5
5-th percentile8.9812292 × 10-5
Q10.001422028
median0.012947939
Q30.0460288
95-th percentile0.062793761
Maximum0.062793761
Range0.062778792
Interquartile range (IQR)0.044606772

Descriptive statistics

Standard deviation0.023465647
Coefficient of variation (CV)1.0175902
Kurtosis-1.3779991
Mean0.023060017
Median Absolute Deviation (MAD)0.012543783
Skewness0.53596217
Sum1540.5475
Variance0.0005506366
MonotonicityNot monotonic
2023-01-03T10:41:35.430928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06279376104 4195
 
6.3%
0.05888692632 3934
 
5.9%
0.05782414753 3863
 
5.8%
0.05236056642 3498
 
5.2%
0.04602879981 3075
 
4.6%
0.04174774721 2789
 
4.2%
0.03870909799 2586
 
3.9%
0.02424931892 1620
 
2.4%
0.02251294794 1504
 
2.3%
0.02058198365 1375
 
2.1%
Other values (148) 38367
57.4%
ValueCountFrequency (%)
1.496871538 × 10-5727
1.1%
2.993743077 × 10-5656
1.0%
4.490614615 × 10-5627
0.9%
5.987486154 × 10-5608
0.9%
7.484357692 × 10-5555
0.8%
8.981229231 × 10-5516
0.8%
0.0001047810077 511
0.8%
0.0001197497231 352
0.5%
0.0001347184385 441
0.7%
0.0001496871538 490
0.7%
ValueCountFrequency (%)
0.06279376104 4195
6.3%
0.05888692632 3934
5.9%
0.05782414753 3863
5.8%
0.05236056642 3498
5.2%
0.04602879981 3075
4.6%
0.04174774721 2789
4.2%
0.03870909799 2586
3.9%
0.02424931892 1620
 
2.4%
0.02251294794 1504
 
2.3%
0.02058198365 1375
 
2.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
66806 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66806
100.0%

Length

2023-01-03T10:41:35.816316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:36.135771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66806
100.0%

Most occurring characters

ValueCountFrequency (%)
0 66806
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66806
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66806
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66806
100.0%

company_low_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
35241 
1
31565 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 35241
52.8%
1 31565
47.2%

Length

2023-01-03T10:41:36.400424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:36.727340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35241
52.8%
1 31565
47.2%

Most occurring characters

ValueCountFrequency (%)
0 35241
52.8%
1 31565
47.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35241
52.8%
1 31565
47.2%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35241
52.8%
1 31565
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35241
52.8%
1 31565
47.2%

state_freq
Real number (ℝ)

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.052535196
Minimum-1
Maximum0.14943269
Zeros0
Zeros (%)0.0%
Negative186
Negative (%)0.3%
Memory size522.0 KiB
2023-01-03T10:41:37.064596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.0046103643
Q10.018127114
median0.035356106
Q30.085321678
95-th percentile0.14943269
Maximum0.14943269
Range1.1494327
Interquartile range (IQR)0.067194563

Descriptive statistics

Standard deviation0.072919209
Coefficient of variation (CV)1.3880068
Kurtosis118.37998
Mean0.052535196
Median Absolute Deviation (MAD)0.022168667
Skewness-8.0645843
Sum3509.6663
Variance0.0053172111
MonotonicityNot monotonic
2023-01-03T10:41:37.476232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1494326857 9983
 
14.9%
0.08915366883 5956
 
8.9%
0.08532167769 5700
 
8.5%
0.05715055534 3818
 
5.7%
0.04656767356 3111
 
4.7%
0.03607460408 2410
 
3.6%
0.03537107445 2363
 
3.5%
0.03535610574 2362
 
3.5%
0.03285633027 2195
 
3.3%
0.03251204982 2172
 
3.3%
Other values (50) 26736
40.0%
ValueCountFrequency (%)
-1 186
0.3%
1.496871538 × 10-51
 
< 0.1%
2.993743077 × 10-54
 
< 0.1%
4.490614615 × 10-53
 
< 0.1%
0.0002245307308 15
 
< 0.1%
0.0002394994462 16
 
< 0.1%
0.0004041553154 27
 
< 0.1%
0.0006137173308 41
 
0.1%
0.0008831542077 59
 
0.1%
0.0009879352154 66
 
0.1%
ValueCountFrequency (%)
0.1494326857 9983
14.9%
0.08915366883 5956
8.9%
0.08532167769 5700
8.5%
0.05715055534 3818
 
5.7%
0.04656767356 3111
 
4.7%
0.03607460408 2410
 
3.6%
0.03537107445 2363
 
3.5%
0.03535610574 2362
 
3.5%
0.03285633027 2195
 
3.3%
0.03251204982 2172
 
3.3%

state_null_flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
66620 
1
 
186

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66620
99.7%
1 186
 
0.3%

Length

2023-01-03T10:41:37.859263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:38.192032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66620
99.7%
1 186
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 66620
99.7%
1 186
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66620
99.7%
1 186
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66620
99.7%
1 186
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66620
99.7%
1 186
 
0.3%

state_low_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
60706 
1
6100 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 60706
90.9%
1 6100
 
9.1%

Length

2023-01-03T10:41:38.484031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:39.123504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 60706
90.9%
1 6100
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 60706
90.9%
1 6100
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60706
90.9%
1 6100
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60706
90.9%
1 6100
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60706
90.9%
1 6100
 
9.1%

zipcode_freq
Real number (ℝ)

Distinct239
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00099392687
Minimum-1
Maximum0.015357902
Zeros0
Zeros (%)0.0%
Negative189
Negative (%)0.3%
Memory size522.0 KiB
2023-01-03T10:41:39.486730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.00031434302
Q10.0012873095
median0.0027692123
Q30.0058827051
95-th percentile0.010523007
Maximum0.015357902
Range1.0153579
Interquartile range (IQR)0.0045953956

Descriptive statistics

Standard deviation0.053421728
Coefficient of variation (CV)53.748147
Kurtosis345.7755
Mean0.00099392687
Median Absolute Deviation (MAD)0.0018561207
Skewness-18.611507
Sum66.400278
Variance0.002853881
MonotonicityNot monotonic
2023-01-03T10:41:39.926589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004355896177 1164
 
1.7%
0.01535790198 1026
 
1.5%
0.01185522258 792
 
1.2%
0.01118163039 747
 
1.1%
0.01076250636 719
 
1.1%
0.01052300692 703
 
1.1%
0.005179175523 692
 
1.0%
0.01013382032 677
 
1.0%
0.009864383439 659
 
1.0%
0.009849414723 658
 
1.0%
Other values (229) 58969
88.3%
ValueCountFrequency (%)
-1 189
0.3%
1.496871538 × 10-542
 
0.1%
2.993743077 × 10-550
 
0.1%
4.490614615 × 10-554
 
0.1%
5.987486154 × 10-5136
0.2%
7.484357692 × 10-5115
0.2%
8.981229231 × 10-5126
0.2%
0.0001047810077 133
0.2%
0.0001197497231 168
0.3%
0.0001347184385 153
0.2%
ValueCountFrequency (%)
0.01535790198 1026
1.5%
0.01185522258 792
1.2%
0.01118163039 747
1.1%
0.01076250636 719
1.1%
0.01052300692 703
1.1%
0.01013382032 677
1.0%
0.009864383439 659
1.0%
0.009849414723 658
1.0%
0.009714696285 649
1.0%
0.008696823639 581
0.9%

zipcode_null_flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
66617 
1
 
189

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66617
99.7%
1 189
 
0.3%

Length

2023-01-03T10:41:40.319677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:40.651537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66617
99.7%
1 189
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 66617
99.7%
1 189
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66617
99.7%
1 189
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66617
99.7%
1 189
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66617
99.7%
1 189
 
0.3%

zipcode_low_flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
53697 
1
13109 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 53697
80.4%
1 13109
 
19.6%

Length

2023-01-03T10:41:40.950543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:41.283224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 53697
80.4%
1 13109
 
19.6%

Most occurring characters

ValueCountFrequency (%)
0 53697
80.4%
1 13109
 
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53697
80.4%
1 13109
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53697
80.4%
1 13109
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53697
80.4%
1 13109
 
19.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
1
55389 
0
11417 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 55389
82.9%
0 11417
 
17.1%

Length

2023-01-03T10:41:41.569925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:41.905906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 55389
82.9%
0 11417
 
17.1%

Most occurring characters

ValueCountFrequency (%)
1 55389
82.9%
0 11417
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55389
82.9%
0 11417
 
17.1%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 55389
82.9%
0 11417
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 55389
82.9%
0 11417
 
17.1%

Older American
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
60723 
1
6083 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 60723
90.9%
1 6083
 
9.1%

Length

2023-01-03T10:41:42.184343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:42.515604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 60723
90.9%
1 6083
 
9.1%

Most occurring characters

ValueCountFrequency (%)
0 60723
90.9%
1 6083
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60723
90.9%
1 6083
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60723
90.9%
1 6083
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60723
90.9%
1 6083
 
9.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
65976 
1
 
830

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65976
98.8%
1 830
 
1.2%

Length

2023-01-03T10:41:42.811615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:43.165097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 65976
98.8%
1 830
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 65976
98.8%
1 830
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65976
98.8%
1 830
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65976
98.8%
1 830
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65976
98.8%
1 830
 
1.2%

Servicemember
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
62302 
1
 
4504

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 62302
93.3%
1 4504
 
6.7%

Length

2023-01-03T10:41:43.431520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:43.763837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 62302
93.3%
1 4504
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 62302
93.3%
1 4504
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62302
93.3%
1 4504
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 62302
93.3%
1 4504
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 62302
93.3%
1 4504
 
6.7%

Closed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
65040 
1
 
1766

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 65040
97.4%
1 1766
 
2.6%

Length

2023-01-03T10:41:44.063034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:44.420423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 65040
97.4%
1 1766
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 65040
97.4%
1 1766
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65040
97.4%
1 1766
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 65040
97.4%
1 1766
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 65040
97.4%
1 1766
 
2.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
1
50928 
0
15878 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 50928
76.2%
0 15878
 
23.8%

Length

2023-01-03T10:41:44.723480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:45.060787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50928
76.2%
0 15878
 
23.8%

Most occurring characters

ValueCountFrequency (%)
1 50928
76.2%
0 15878
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 50928
76.2%
0 15878
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 50928
76.2%
0 15878
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 50928
76.2%
0 15878
 
23.8%

Closed with monetary relief
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
61832 
1
 
4974

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61832
92.6%
1 4974
 
7.4%

Length

2023-01-03T10:41:45.331040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:45.705325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 61832
92.6%
1 4974
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 61832
92.6%
1 4974
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61832
92.6%
1 4974
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61832
92.6%
1 4974
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61832
92.6%
1 4974
 
7.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
58199 
1
8607 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 58199
87.1%
1 8607
 
12.9%

Length

2023-01-03T10:41:45.955440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:46.300725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 58199
87.1%
1 8607
 
12.9%

Most occurring characters

ValueCountFrequency (%)
0 58199
87.1%
1 8607
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58199
87.1%
1 8607
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58199
87.1%
1 8607
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58199
87.1%
1 8607
 
12.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size522.0 KiB
0
66275 
1
 
531

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66806
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66275
99.2%
1 531
 
0.8%

Length

2023-01-03T10:41:46.574257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-03T10:41:46.916063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66275
99.2%
1 531
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 66275
99.2%
1 531
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66806
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66275
99.2%
1 531
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 66806
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66275
99.2%
1 531
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66275
99.2%
1 531
 
0.8%

Interactions

2023-01-03T10:41:15.739478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:49.043680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:52.216395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:55.727360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:58.952248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:02.213402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:05.461569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:09.003756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:12.400454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:16.100786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:49.377661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:52.563835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:56.060890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:59.284814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:02.569951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:05.789274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:09.330430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:12.764378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:16.488317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:49.728379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:52.927848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:56.422668image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:59.652373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:02.922081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:06.163918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:09.719366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:13.141539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:16.827612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:50.062840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:53.261246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:56.761587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:00.006771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:03.253690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:06.526047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:10.086659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:13.478685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:17.181988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:50.442493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:53.636346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:57.127809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:00.370943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:03.612747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:07.130322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:10.510179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:13.867416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:17.499018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:50.788955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:53.977938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:57.473900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:00.708339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:03.985147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:07.517734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:10.892339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:14.214761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:17.848825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:51.146129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:54.358564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:57.854895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:01.088740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:04.336760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:07.886056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:11.275474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:14.627705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:18.208121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:51.519663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:54.742506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:58.217208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:01.470988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:04.714443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:08.270182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:11.649890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:15.012626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:18.551365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:51.874208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:55.352720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:40:58.599600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:01.851559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:05.098262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:08.633339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:12.016473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T10:41:15.396433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-03T10:41:47.313845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
labeldays_between_receipt_and_sentsub_product_freqissue_freqsub_issue_freqcompany_public_response_freqcompany_freqstate_freqzipcode_freqproducttimely_responseconsumer_disputed?sub_product_null_flagsub_product_low_flagissue_low_flagsub_issue_null_flagsub_issue_low_flagcompany_public_response_null_flagcompany_public_response_low_flagcompany_low_flagstate_null_flagstate_low_flagzipcode_null_flagzipcode_low_flagNot Older American, Not ServicememberOlder AmericanOlder American, ServicememberServicememberClosedClosed with explanationClosed with monetary reliefClosed with non-monetary reliefUntimely response
label1.0000.0280.4430.2090.041-0.134-0.2500.002-0.0071.0000.1980.0581.0000.6150.7061.0000.5950.1650.1480.5750.0550.0260.0540.0290.0590.1020.0430.0860.1230.2060.3410.2110.111
days_between_receipt_and_sent0.0281.000-0.025-0.0490.030-0.048-0.152-0.012-0.0160.0170.0510.0180.0090.0000.0000.0390.0200.0110.0270.0350.0000.0080.0000.0070.0000.0000.0000.0000.0210.0060.0180.0040.072
sub_product_freq0.443-0.0251.0000.148-0.051-0.115-0.4830.003-0.0011.0000.1110.0201.0000.1760.2040.1590.0090.0990.1060.4840.0000.0090.0000.0000.0130.0210.0190.0370.0940.0950.0570.1370.056
issue_freq0.209-0.0490.1481.0000.5710.0500.1330.0150.0160.5700.1170.0510.5230.1690.8630.7610.3530.0950.0880.2730.0260.0220.0260.0380.0290.0720.0240.0550.0700.1440.2490.1580.061
sub_issue_freq0.0410.030-0.0510.5711.0000.009-0.001-0.0130.0041.0000.1020.0330.1590.1400.3311.0000.4500.0220.0630.0700.0130.0220.0120.0170.0080.0720.0160.0620.0280.0000.2270.1510.059
company_public_response_freq-0.134-0.048-0.1150.0500.0091.0000.2290.0070.0210.2160.1050.0280.2650.0590.0610.1000.0911.0000.8800.3620.0140.0000.0140.0140.0130.0330.0020.0440.0630.0900.0700.1100.088
company_freq-0.250-0.152-0.4830.133-0.0010.2291.0000.0300.0310.4580.2070.0310.7560.1660.2960.6220.2470.4270.2000.9670.0250.0300.0250.0340.0230.0660.0190.0720.1300.1870.2430.2430.108
state_freq0.002-0.0120.0030.015-0.0130.0070.0301.0000.4330.0480.0100.0000.0000.0250.0100.0180.0250.0060.0150.0261.0000.3410.9920.1940.0280.0110.0200.0420.0000.0000.0150.0040.005
zipcode_freq-0.007-0.016-0.0010.0160.0040.0210.0310.4331.0000.0610.0000.0000.0000.0180.0100.0120.0000.0070.0060.0080.9890.0150.9970.0260.0040.0070.0000.0000.0000.0000.0140.0050.000
product1.0000.0171.0000.5701.0000.2160.4580.0480.0611.0000.2000.0581.0000.6150.7071.0000.5950.1740.1500.5880.0620.0270.0610.0370.0600.1040.0440.0940.1270.2180.3610.2110.112
timely_response0.1980.0510.1110.1170.1020.1050.2070.0100.0000.2001.0000.0340.1110.0070.0290.1020.0730.0820.0260.1790.0040.0060.0000.0020.0000.0200.0030.0170.0950.0790.0370.0460.488
consumer_disputed?0.0580.0180.0200.0510.0330.0280.0310.0000.0000.0580.0341.0000.0200.0060.0190.0330.0090.0190.0300.0070.0000.0080.0000.0150.0150.0100.0080.0150.0310.1170.0780.0900.049
sub_product_null_flag1.0000.0091.0000.5230.1590.2650.7560.0000.0001.0000.1110.0201.0000.1760.2040.1590.0090.0990.1060.4840.0000.0090.0000.0000.0130.0210.0190.0370.0940.0950.0570.1370.056
sub_product_low_flag0.6150.0000.1760.1690.1400.0590.1660.0250.0180.6150.0070.0060.1761.0000.2020.1400.0550.0540.0040.1250.0180.0000.0180.0020.0480.0000.0110.0630.0090.0320.0750.0210.004
issue_low_flag0.7060.0000.2040.8630.3310.0610.2960.0100.0100.7070.0290.0190.2040.2021.0000.3310.0570.0330.0380.0260.0100.0000.0100.0020.0060.0160.0130.0230.0460.0800.2060.0340.013
sub_issue_null_flag1.0000.0390.1590.7611.0000.1000.6220.0180.0121.0000.1020.0330.1590.1400.3311.0000.4500.0220.0630.0700.0130.0220.0120.0170.0080.0720.0160.0620.0280.0000.2270.1510.059
sub_issue_low_flag0.5950.0200.0090.3530.4500.0910.2470.0250.0000.5950.0730.0090.0090.0550.0570.4501.0000.0380.0510.0610.0000.0180.0000.0210.0130.0410.0060.0290.0080.0160.0780.0200.055
company_public_response_null_flag0.1650.0110.0990.0950.0221.0000.4270.0060.0070.1740.0820.0190.0990.0540.0330.0220.0381.0000.2010.0660.0060.0000.0070.0050.0070.0090.0040.0000.0150.0800.0370.1020.088
company_public_response_low_flag0.1480.0270.1060.0880.0630.8800.2000.0150.0060.1500.0260.0300.1060.0040.0380.0630.0510.2011.0000.1730.0050.0060.0060.0100.0000.0190.0000.0210.0360.0040.0130.0080.017
company_low_flag0.5750.0350.4840.2730.0700.3620.9670.0260.0080.5880.1790.0070.4840.1250.0260.0700.0610.0660.1731.0000.0090.0160.0080.0200.0000.0540.0070.0560.1050.0380.0470.0860.094
state_null_flag0.0550.0000.0000.0260.0130.0140.0251.0000.9890.0620.0040.0000.0000.0180.0100.0130.0000.0060.0050.0091.0000.0160.9890.0250.0040.0080.0000.0000.0000.0000.0140.0050.000
state_low_flag0.0260.0080.0090.0220.0220.0000.0300.3410.0150.0270.0060.0080.0090.0000.0000.0220.0180.0000.0060.0160.0161.0000.0150.2690.0270.0030.0300.0330.0000.0090.0020.0030.010
zipcode_null_flag0.0540.0000.0000.0260.0120.0140.0250.9920.9970.0610.0000.0000.0000.0180.0100.0120.0000.0070.0060.0080.9890.0151.0000.0260.0040.0070.0000.0000.0000.0000.0140.0050.000
zipcode_low_flag0.0290.0070.0000.0380.0170.0140.0340.1940.0260.0370.0020.0150.0000.0020.0020.0170.0210.0050.0100.0200.0250.2690.0261.0000.0330.0160.0170.0240.0110.0000.0000.0000.010
Not Older American, Not Servicemember0.0590.0000.0130.0290.0080.0130.0230.0280.0040.0600.0000.0150.0130.0480.0060.0080.0130.0070.0000.0000.0040.0270.0040.0331.0000.6970.2470.5920.0000.0050.0090.0000.000
Older American0.1020.0000.0210.0720.0720.0330.0660.0110.0070.1040.0200.0100.0210.0000.0160.0720.0410.0090.0190.0540.0080.0030.0070.0160.6971.0000.0350.0850.0110.0030.0170.0120.006
Older American, Servicemember0.0430.0000.0190.0240.0160.0020.0190.0200.0000.0440.0030.0080.0190.0110.0130.0160.0060.0040.0000.0070.0000.0300.0000.0170.2470.0351.0000.0300.0140.0000.0100.0000.008
Servicemember0.0860.0000.0370.0550.0620.0440.0720.0420.0000.0940.0170.0150.0370.0630.0230.0620.0290.0000.0210.0560.0000.0330.0000.0240.5920.0850.0301.0000.0040.0030.0290.0130.002
Closed0.1230.0210.0940.0700.0280.0630.1300.0000.0000.1270.0950.0310.0940.0090.0460.0280.0080.0150.0360.1050.0000.0000.0000.0110.0000.0110.0140.0041.0000.2950.0460.0630.014
Closed with explanation0.2060.0060.0950.1440.0000.0900.1870.0000.0000.2180.0790.1170.0950.0320.0800.0000.0160.0800.0040.0380.0000.0090.0000.0000.0050.0030.0000.0030.2951.0000.5080.6890.160
Closed with monetary relief0.3410.0180.0570.2490.2270.0700.2430.0150.0140.3610.0370.0780.0570.0750.2060.2270.0780.0370.0130.0470.0140.0020.0140.0000.0090.0170.0100.0290.0460.5081.0000.1090.025
Closed with non-monetary relief0.2110.0040.1370.1580.1510.1100.2430.0040.0050.2110.0460.0900.1370.0210.0340.1510.0200.1020.0080.0860.0050.0030.0050.0000.0000.0120.0000.0130.0630.6890.1091.0000.034
Untimely response0.1110.0720.0560.0610.0590.0880.1080.0050.0000.1120.4880.0490.0560.0040.0130.0590.0550.0880.0170.0940.0000.0100.0000.0100.0000.0060.0080.0020.0140.1600.0250.0341.000

Missing values

2023-01-03T10:41:19.344349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-03T10:41:21.322336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

productconsumer_complaint_narrativetimely_responseconsumer_disputed?labeldays_between_receipt_and_sentsub_product_freqsub_product_null_flagsub_product_low_flagissue_freqissue_low_flagsub_issue_freqsub_issue_null_flagsub_issue_low_flagcompany_public_response_freqcompany_public_response_null_flagcompany_public_response_low_flagcompany_freqcompany_null_flagcompany_low_flagstate_freqstate_null_flagstate_low_flagzipcode_freqzipcode_null_flagzipcode_low_flagNot Older American, Not ServicememberOlder AmericanOlder American, ServicememberServicememberClosedClosed with explanationClosed with monetary reliefClosed with non-monetary reliefUntimely response
0Debt collectionXXXX has claimed I owe them {$27.00} for XXXX years despite the PROOF of PAYMENT I sent them : canceled check and their ownPAID INVOICE for {$27.00}! \nThey continue to insist I owe them and collection agencies are after me. \nHow can I stop this harassment for a bill I already paid four years ago? \n10400.078631000.11256500.02933900-1.000000100.002634010.057151000.00079301010001000
1Consumer LoanDue to inconsistencies in the amount owed that I was told by M & T Bank and the amount that was reported to the credit reporting agencies, I was advised to write a good will letter in order to address the issue and request the negative entry be removed from my credit report all together. I had a vehicle that was stolen and it was declared a total loss by insurance company. The insurance company and the GAP insurancw companypaid the outstanding balance of the loan, but I was told by M & T Bank that there was still a balance due on the loan. In good faith, without having received any proof as to why there was still a balance, I made a partial payment towards the remaining debt. I then sent the goodwill letter still offering to pay the remainder of the debt, but in exchange for the removal of the negative entry on my credit report. At one point, in XXXX 2015, per my credit monitoring agency, it showed a delinquent balance of {$0.00}, but when I checked my credit report again on XXXX XXXX 2015, there was a delinquent balance of {$1400.00}. The monies from the GAP insurance and my insurance company has been paid, M & T Bank says that I still owe {$620.00}, of which {$210.00} has already been paid. I contacted M & T Bank via return receipt mail, but I have not been given the courtesy of a response yet. \n10100.030910000.0246980-1.00000010-1.000000100.002679010.032856000.00293400000101000
2MortgageIn XX/XX/XXXX my wages that I earned at my job decreased by almost half, by XX/XX/XXXX I knew I was in trouble with my home loan. I began contacting WFB whom my home loan is with, for assitance and options. \nIn early XX/XX/XXXX I began the Loan Modification process with Wells Fargo Bank. I was told that they would not assist me with anything financial on my home loan until I fell 90 days behind, though at the time I started to inquire for assistance from WFB I was only a few weeks behind. So, I began working with a program called XXXX. They approved me for a variety of assistence and reached out to Wells Fargo Bank to determine what they could assist with. Wells Fargo then turned down the assistance from XXXX and finally offered to do a Loan Modification for me. The outcome was totally unknow about what I would be offered in the end by WFB for assistance. Wells Fargo lost my paperwork twice during this process, so it took 2 months from the time I started to the time my paperwork began to be processed for some kind of approval. In XX/XX/XXXX I was in a trial period of 3 months of payments based on a slightly modified amount. Roughly {$75.00} less than what I was paying on my original payment. However, my caseworker with WFB, failed to tell me that since the payments during this time were not the full amount of the original mortgage payment so they were not applied to the loan. I discovered this in XX/XX/XXXX when I was told by a creditor with XXXX that she could n't restore my line of credit because of something to do with my mortgage. I then called and found this out. My caseworker at WFB then told me it was n't her responsibility to tell me this information. I told her that she could have told me that if I could come up with the remaining amount of {$75.00} then the payments would be applied. Instead, she chose to withhold this information and damage my credit even further. Now we are into 5 months ( including the 90 days WFB told me I had to fall behind until they would help me ) without an applied payment to my home loan, going on 6 months. \nMore paperwork was lost in XXXX as the modification was being finalized and my loan modification did n't go through until XX/XX/XXXX. I spoke to a lawyer that is working on a class-action lawsuit against WFB for people in a similar situation and said this paper loss is typical. \nWells Fargo did not reduce the interest rate, but they did reduce the payment by less than {$100.00} and the payments I was behind went back into the loan. \nMy property taxes also fell behind in my escrow account at this time because there was no payment on the loan. So, I am behind on my property taxes, have damaged credit, and and I am not in any better payment situation than I was. \nThey can not give me a reason on why they rejected XXXX assistance. I was originally told they were not an approved agency but on a later phone call to Wells Fargo Bank I was told that they work with that program often and the WFB employee could not understand why they would reject the help. I also spoke with XXXX and they said that Wells Fargo works with this program all the time and could n't understand why they would do this. \nI feel that I have been taken advantage of by Wells Fargo and would like to pursue legal action against them based on negligence. \nAlthough my payments are back on track I am not making much more money now than I was before so it is only a matter of time before something like this happens again if something unforeseen happens to my life that affects my finances. \n11600.099377000.0772830-1.00000010-1.000000100.046029000.149433000.00229000100001000
3MortgageI have an open and current mortgage with Chase Bank # XXXX. Chase is reporting the loan payments to XXXX but XXXX is surpressing the information and reporting the loan as Discharged in BK. This mortgage was reaffirmed in a Chapter XXXX BK discharged dated XXXX/XXXX/2013. Chase keeps referring to BK Law for Chapter XXXX and we keep providing documentation for Chapter XXXX, and the account should be open and current with all the payments \n11600.099377000.0981050-1.00000010-1.000000100.038709000.149433000.01076300010001000
4MortgageXXXX was submitted XX/XX/XXXX. At the time I submitted this complaint, I had dealt with Rushmore Mortgage directly endeavoring to get them to stop the continuous daily calls I was receiving trying to collect on a mortgage for which I was not responsible due to bankruptcy. They denied having knowledge of the bankruptcy, even though I had spoken with them about it repeatedly and had written them repeatedly referencing the bankruptcy requesting them to cease the pursuit, they continued to do so. When they were unable to trick me into paying, force me into paying in retaliation they placed reported to my credit bureaus a past due mortgage amount that had been discharged in Federal Court. On XX/XX/XXXX Rushmore responded the referenced complaint indicating that they would remove the reporting from my bureau, yet it is still there now in XX/XX/XXXX. I would like them to remove it immediately and send me a letter indicating that it should not have been there in the first place and they are going to remove it from all my bureaus. Rushmore, when speaking to me, represented themselves as the new note holder, but when CFPB was involved, they identified themselves as the servicing agency for XXXX XXXX XXXX. This credit bullying and racial discrimination practices is damaging to anyone who is exposed to these tactics and this needs to stop. Them denying their intent and then walking away with no penalties of any kind is one of the reasons it continues. Please assist me in procuring the resolution once and for all. \n11600.099377000.0085171-1.00000010-1.000000100.001227010.149433000.00420600010001000
5MortgageExperian is reporting my OPEN and CURRENT Mortgage loan with XXXX as XXXX in BK. XXXX is reporting the monthly payments but Experian is supressing the information and refuses to accurately report the payments and show the account is Open. This debt was Officially reinstated in a Chapter XXXX BK. This is NOT a Chapter XXXX BK and Experian has no legal right to not rport the current status of this mortgage \n10660.099377000.0981050-1.000000100.289031000.058887000.149433000.01076300010001000
6MortgageThis complaint is against Wells Fargo Bank for violations of the terms and conditions of Federal government 's mortgage modification program ( HARP ). Violations of the law include but are not limited to the following:1 ) A blatant miscalculation of income. ( Adding a 25 % increase in income on a household contributor even when it was not supported by fact, law, or the documents submitted ) 2 ) Taking longer than XXXX days to deny the appeal. \n3 ) Not following the guidelines for the application and appeal process set forth by the Federal government for loan modifications. \n4 ) Wells Fargo falsely falsely accused customer of not submitting enough documentation to support appeal of original denial. \n5 ) Customer we given false and conflicting reasons for denial. \n10600.099377000.0772830-1.00000010-1.000000100.046029000.149433000.00049401100001000
7MortgageI spoke to XXXX of green tree representatives on XXXX XXXX XXXX, 2014 about the annual escrow acct disclosure statement in reference to the PMI on my loan I recieved. According to my original documents with XXXX my PMI should be {$120.00}. However the loan was sold to Green tree. About XXXX months ago I noticed that Green tree is deducting out my escrow acct {$220.00} which i never agree to pay to Green tree, I have the supporting documents of what i signed in referance to this loan and the terms of the PMI. I would like this to be taken care of as soon as possible, as it stresses me out to think that Green tree is XXXX me off. I have asked and i expect a refund for all this time that i have been paying the wrong amount. I called Green tree a handful of times about this issue i have also fax them a letter showing my supporting documents and dont believe that now after more than 60 days of calling, and writing to them and this has still not been taken care of. It is Now XXXX XXXX which is more than enough time since this was brought their attention! This is XXXX more attemp to try and get a honest and fair response. I dont believe green tree is handleing this in a professional manner and I am very disappointed at not getting any responce at all about this matter. In all honesty it is looking to me that green tree has made a mistake here and is not ready to fix it and compensate me because if its was something i did owe on PMI they would have brought proof to me quickly. I would appriciate any kind of attempt to fix this \n10600.099377000.0981050-1.00000010-1.000000100.015987000.149433000.00079301100000010
8Credit cardi opened XXXX Bank of America credit cards 15-20 years ago and the interest on the account is very low, some time 7 years ago or so i got a notice that my interest was changing and going up to prime plus 2 % i think.prime was going up back then, and several banks did this, or you had a option to close the account, and stay with the old terms and cond and lock in the interest rate till it was paid off.so the new terms and cond i was not accepting.i closed all XXXX accounts and have made payments since then on time never late.my history will show this.i each month for the past 5 years plus i schedule all my payments online on the XXXX of ea month and get a conformation number that the payment has been scheduled.on XXXX i did this to XXXX-accounts as i have done for 5-years, got a conformation number on all transactions.XXXX payment was due XXXX get a call at my home XXXX from a very rude man with a XXXX # on caller ID and says he is calling from XXXX card services, and my payment was late, i have a late charge, i have not meant my terms and cond and i was being charged off and IRS was getting sent a statement.i XXXX thought it was a scam. then he tells me that XXXX is owned by Bank of America he still was nasty, threaten me over now 7-days past due.i got nasty with him, explained i scheduled my payments, and i have a conformation # and he did not care.after 1hr plus with him haurasing me, i hung up and called Bank of America customer service, where after 6.5hrs later on the phone with people i get told from the internet dept that they saw i scheduled all my payments, they said Bank of America was wrong here,100 % but he did not know how to fix problem.apparently some time the XXXX week in XXXX they put in a new billing system, and when they did a system update, they took all XXXX accounts and put them in a un active position, so my payments scheduled were there but they could not not find my accounts any more to apply payment..i made them on XXXX XXXX, yet on XXXX the new system could not apply my payment.i had no way of knowing this, i find out that all XXXX accounts are this way now, when i ask them to fix this issue, they have no idea how to fix the problem, the people who installed the new updated system had to input me XXXX then my accounts could be seen.i was suppose to get a call from someone at corp level the next day to address this problem, that never happened, so XXXX i spend another 5 hours on the phone, and by the way i had XXXX-phones going, my XXXX call was on my home phone, where i was put on hold to speak to supervisor,2hrs later never happened, i called on cell phone, and got a supervisor who did nothing for me for 3 hours.i asked for 1-thing never got, i asked for a {$25.00} credit for late fee she refused this, she kept reading notes, puts me on hold comes back with nothing i tried to explain what needed to be done but she would not listen, after 3 hours with her and 12 hours of getting no where, i am filling a complaint, and i also am concern, have not.have not spoken to any attorney yet, but i believe my rights have been violated, when i get a call from XXXX and threaten, and spoke to as i was, and to be told from XXXX XXXX in XXXX AZ internet dept that Bank of America was at fault, and they needed to fix, now i am told XXXX-payments are late i have fees and they refuse any service, or refuse to wave fees, and it is their fault.i have all conformation # and all payments have always been made on time, never late, yet i am getting called at home, threaten, over a Bank of America wrong doing, and i am paying for it now.this is wrong, and i really feel i have been violated in this way.i also found out supervisors i spoke to never recorded that they ever spoke with me, that being XXXX XXXX call center S.Carolina.and she was to call me and never did, and never put in notes she spoke to me this is what i get, and it is not right at all.thank you for your time. \n1120-1.000000100.0123940-1.000000100.289031000.052361000.011301000.00076301100000100
9Consumer LoanI applied for a loan with XXXX XXXX and had purchased XXXX vehicle during the month of XXXX 2014. I was told that I would be getting a good deal on the vehicles in which i purchased. I later found out that the interest rate charged for the loans at the time was almost XXXX times that of the average consumer for the credit score that I had and when I came back to speak to the finance manager. The dealership had let her go for fudging many of the loans and jacking up many customers interest in order to make a higher commission and she was no longer employed with the company due to her increasing many loans on the interest that some of their consumers were paying. The dealership as of today 's date has never contacted me and had i not inquired I would have never known the truth. When I applied my credit score was showing XXXX approximately and thought that I would get the lowest interest rate at the dealership offered but instead I received an interest rate that was nearly XXXX of that of the regular consumer. I am requesting an investigation on the lending of loans and services of this company based on the following Discrimination based on race, and color, gender for I was charged a higher rate than other consumers of a different race, gender or color I request an immediate investigation on the loans that were processed during the time that I applied and it will show this to be a everyday factor practice based on race and to investigate the transactions of this company and how they are advertising a certain price and then selling their vehicles for more than the advertised price. It is like a bait and switch on the consumer. Predatory lending has clearly been an everyday factor in order for the finance department to make a better commissions on the consumer who is not aware of how the process works. I was charged a higher rate for no reason other than greed. Fair and accurate transaction was not taken into account by dealership. Equal credit opportunity act has been was not taken into account. Gramm leach biley act was not taken into account. After purchasing the vehicle I was called back into the office to sign more paperwork again after the vehicle was in my possession for more than a month. I was told that it was necessary in order to get my military discount. I never got the discount on my vehicles and the price of the vehicles was sold for more than they advertised which I later found out from the XXXX ledger. I am still trying to figure out where is the discount and why was I charged a higher interest rate and why did the finance manager deliberately fudge my loan. consumers of a different race who have purchased from this dealership have lower interest rates than mine with lower credit scores than what I had when I purchased my vehicle. As of today 's date their is no discount, higher interest rate, higher price paid for vehicles and the military rate was applied later and went somewhere else for all I know. It is truly a shock to me to see a dealership do this to a consumer. \n10100.030910000.0091161-1.00000010-1.000000100.001003010.005059010.00080801100001000
productconsumer_complaint_narrativetimely_responseconsumer_disputed?labeldays_between_receipt_and_sentsub_product_freqsub_product_null_flagsub_product_low_flagissue_freqissue_low_flagsub_issue_freqsub_issue_null_flagsub_issue_low_flagcompany_public_response_freqcompany_public_response_null_flagcompany_public_response_low_flagcompany_freqcompany_null_flagcompany_low_flagstate_freqstate_null_flagstate_low_flagzipcode_freqzipcode_null_flagzipcode_low_flagNot Older American, Not ServicememberOlder AmericanOlder American, ServicememberServicememberClosedClosed with explanationClosed with monetary reliefClosed with non-monetary reliefUntimely response
66796MortgageMy mortgage company Ocwen loan services keeps repeatedly calling our home after XXXX. They have called three times after XXXX. When we have asked them to call during normal calling hours they claim that they are. Tonight I received a call at XXXX and last night they called at XXXX. This is harassment and I would like for it to stop immediately. \n10600.035596000.0981050-1.00000010-1.000000100.024249000.010598000.00113800100001000
66797Credit reportingBanks tell me I have home morgages open when I have never bought a house. \n1030-1.000000100.01544800.00874201-1.000000100.062794000.149433000.00681100000101000
66798Debt collectionThis debt collection is for a XXXX XXXX. \n\nMy wife XXXX XXXX XXXX was married to a XXXX XXXX - who died 17 years ago - they had NO children together! XXXX XXXX is a child of XXXX from a previous marriage that was divorced. XXXX former wife had custody of XXXX My wife and I have been married for 15 years. I have never met XXXX and my wife has never had a relationship with XXXX - even when she was married to XXXX! \n\nWe get harassing phone calls from debt collectors for XXXX XXXX and her husband - Daily! on both of our phone lines! We have requested they remove our contact info - but this stops for a period and then restarts. the caller harass us and threaten us - the only info I have for the collection company is - Capital Accounts, XXXX XXXX, PA - phone XXXX \n10400.056806000.11256500.068227000.098329000.000524010.035371000.00130200100001000
66799Credit reportingI have not applied for credit at multiple institutions that are stated on my credit report as a hard credit inquiry. \n1030-1.000000100.12501900.039817000.009894010.001063010.029563000.00016501100001000
66800Debt collectionReceived a letter from Afni, Inc. attempting to collect an alleged outstanding debt owed to XXXX XXXX XXXX when I have never had an account with XXXX XXXX XXXX for any product or service. \n10400.078631000.11256500.06822700-1.000000100.002979010.031255000.00067401100001000
66801Credit reportingXXXX XXXX is reporting incorrectly, payments have been on time, the vehicle was turned in on time to the dealership. \n1030-1.000000100.12501900.033769000.289031000.057824000.022723000.00480500100001000
66802Credit reportingReflecting incorrect payment status. Have been on time. \n1030-1.000000100.12501900.033769000.289031000.057824000.022723000.00480500100001000
66803Payday loanI have been paying {$180.00} a month through direct debit withdrawal from my checking account for several months on a {$600.00} loan. The {$180.00} a month was an " Extension '' that I thought was installments to satisfy the original loan. This company has received {$2000.00} in fees on a {$600.00} loan and I still have a balance due. They did send me a contract that I agreed to in haste and did not clearly understand. Although I understand payday loans, how is it fair business to charge this amount on a {$600.00} loan? This equates to over 300 % interest in just an extension fee??? There should be a cap on what they can charge you for fees.. {$180.00} a month as an " Extension Fee ''!! XXXX \n10800.024818000.00497010.00420601-1.000000100.000135010.015777000.00984900000101000
66804MortgageI recently became aware that Amerisave Mortgage Corporation participated in some illegal practices that harm customers. I did not know at the time ( until recently ) that I had any legal recourse for an issue I had, and I would like to seek restitution at this time. I applied for a 30 year fixed rate mortgage loan with Amerisave on XXXX/XXXX/06 ( loan application # XXXX ). As part of the process I paid for an appraisal. I also paid for " discount points '' to lower the interest rate on the mortgage. These payments were made through my credit card on XXXX/XXXX/06, and I have those records. Shortly after I cancelled the loan, as the terms had not been satisfactory. It was my understanding that the {$390.00} appraisal fee was non-refundable, but the {$3000.00} that was paid in discount points should have been reimbursed. I inquired many times to Amerisave to get this {$3000.00} reimbursement and they said they could not refund the money. Not only was this bad customer service, but caused customer harm to me in a deceptive and possibly illegal action of not returning the {$3000.00} in funds. Although it has been nearly 10 years, I would like to have this money refunded to me. I did not know at the time that I had any rights to get this money back, and was told to the contrary. Please advise. Thank you. \n11600.099377000.0241890-1.000000100.098329000.000404010.024324000.00455000100001000
66805MortgageBank of America has demonstrated an on-going level of incompetence, callous disregard for the wellbeing of its customer and negligence in their responsibility to adequately guide a client through a loan process. Because of their unprofessional behavior we have suffered extraordinary emotional hardship and they have inflicted a financial burden on us not initially intended when we applied for the loan. \n\nOne of many issues is as follows. After going through an extraordinarily difficult mortgage finalization process I wrote to the president of Bank of America. Within a very short time I received calls from a corporate representative in XXXX, another representative in XXXX and a regional District Sales Manager. \n\nMy issue, at this point, is the way they have opted to getting around paying what they said they would pay me. In response to my complaint they agreed to cover the cost of the house appraisal and to honor their " guaranteed close '' agreement. In a discussion with the District Sales Manager he stated that a reimbursement of travel costs due to their inadequate notification that our initial close date could not be met. We discussed the rates to be utilized to arrive at a figure and he said that actual receipts would not be needed. He asked if this approach would be acceptable. I told him to email all that we have talked about and I will review it with my wife. Here is what the representative from the corporate office wrote me soon after : " XXXX XXXX, I spoke with XXXX and he asked me to pass along this information to you regarding your request for reimbursement. If you have any questions or need anything additional, please feel free to reach out to XXXX at XXXX. I have cc 'd him on this email as well. \nRegarding Reimbursement : There were XXXX trips back and forth between XXXX and XXXX. The trip is approximately XXXX miles. This brings the total mileage to XXXX miles. At $ XXXX/mile that would bring the total mileage reimbursement to {$620.00}. \nThe hotel room was {$87.00} a night for XXXX nights totaling a hotel reimbursement in the amount of {$170.00}. Per diem for food is $ XXXX/day/person. For XXXX people over a 4 day period that brings the total food reimbursement to {$600.00}. \nThis brings the total reimbursement to just under {$1400.00}. \nWe have already agreed to refund your appraisal fee in the amount of {$460.00}, which is currently in process. You are also going to be receiving your Close On Time Commitment Payout in the amount of {$500.00}. This brings the total reimbursement that is already in process to {$960.00}. In order to bring us to the {$1400.00} total, we will be issuing you a check in the amount of {$440.00} to cover the difference. \n{$620.00} ( mileage ) + {$170.00} ( lodging ) + {$600.00} ( food cost ) = {$1300.00} ( Rounded up to {$1400.00} ) {$1400.00} ( agreed reimbursement ) - {$460.00} ( appraisal refund in process ) - {$500.00} ( COTC in process ) = {$440.00} We will issue this check to the new address. Can you please confirm this is where you would like to have this mailed? \nThank you very much and have a nice weekend! \nSincerely, Consumer Lending Client Experience '' As you can see they created a final reimbursement figure by taking one issue, the travel reimbursement and then subtracting the " in-process '' reimbursements. My strong contention is that there are three separate and distinct issues to be reimbursed. The three issues would total {$2300.00}. By doing a subtraction of two previous reimbursements for different issues they have substantially reduced their total payout. I find such an action to be cheap, insulting and a truly unprofessional way to address the needs of an aggrieved party to a horrific mortgage loan process as reviewed in my letter to the president ( attached ). A financial institution with the reach and impact that Bank of America has must be held to a higher standard and account for its actions. \n11600.099377000.0241890-1.000000100.055444000.052361000.046568000.01535800100001000

Duplicate rows

Most frequently occurring

productconsumer_complaint_narrativetimely_responseconsumer_disputed?labeldays_between_receipt_and_sentsub_product_freqsub_product_null_flagsub_product_low_flagissue_freqissue_low_flagsub_issue_freqsub_issue_null_flagsub_issue_low_flagcompany_public_response_freqcompany_public_response_null_flagcompany_public_response_low_flagcompany_freqcompany_null_flagcompany_low_flagstate_freqstate_null_flagstate_low_flagzipcode_freqzipcode_null_flagzipcode_low_flagNot Older American, Not ServicememberOlder AmericanOlder American, ServicememberServicememberClosedClosed with explanationClosed with monetary reliefClosed with non-monetary reliefUntimely response# duplicates
39Credit reportingI have sent several requests to Experian requesting an investigation of my accounts. It has been months and I still have not received a response to my concerns. The only thing I have received is some automated rejection letter stating that they wo n't do anything to help me ( please see attached ). \nThis has to be a violation of my rights. I feel like I 've already wasted so much time just trying to get Experian to look at the errors on my credit report and I 'm so frustrated that Experian is intentionally not responding to my inquiries. I need to have this issue resolved immediately. There are many things I need to do with my life and they all involve my credit. But I am not able to move forward all because of this credit bureau! \n1030-1.0100.02978800.011795000.289031000.058887000.015777000.0098490010000100018
77Credit reportingWhile checking my personal credit report, I noticed an unauthorized and fraudulent credit inquiry made by XXXX on or about XX/XX/XXXX on Transunion. I did not authorized anyone employed by this company to make any inquiry and view my credit report. XXXX has violated the Fair Credit Reporting Act Section 1681b ( c ). They were not legally entitled to make this fraudulent inquiry. This is a serious breach of my privacy rights. \nI have requested that they mail me a copy of my signed authorization form that gave them the right to view my credit within five ( 5 ) business days so that I can verify its validity and advised them that if they can not provide me with proof that I authorized them to view my credit report then I am demanding that they contact the credit bureaus immediately and have them remove the unauthorized and fraudulent hard inquiry immediately. I also requested that they remove my personal information from their records. My Social Security # is XXXX and my Date of Birth is XX/XX/XXXX in case it is needed to locate the fraudulent inquiry in their system. \n1031-1.0100.01029800.009565010.289031000.057824000.149433000.0074840000010100011
21Credit reportingI am filing this complaint because I think what Experian is doing to me is wrong, unethical and may be against the law. A letter was mailed to Experian on or around [ XXXX/XXXX/15 ]. \nI clearly stated my concerns to them regarding inaccurate, questionable or unverifiable information listed on my credit report. I also provided a clear copy of my ID, proof of my social security number and a proof of my current mailing address. \nOn [ XXXX/XXXX/15 ] I received a notice from Experian stating the following : '' We received a suspicious request in the mail regarding your personal credit report and determined that it was not sent by you. Suspicious requests are reviewed by Experian security personnel who work regularly with law enforcement officials and regulatory agencies to identify fraudulent and deceptive correspondence purporting to originate from consumers. \nIn an effort to safeguard your personal credit information from fraud, we will not be initiating any disputes based on the suspicious correspondence. \nExperian will apply this same policy to any future suspicious requests that we receive regarding your personal credit information, but we will not send additional notices to you of suspicious correspondence. \nIf you believe that information in your personal credit report is inaccurate or incomplete, please visit our website at experian.com/validate dispute or call us at XXXX ( XXXX ) XXXX to speak directly to an Experian consumer assistance representative. " However, an Experian confirmation number was not even provided on this notice. I can not imagine what could have possibly been " suspicious '' about my correspondence to Experian. I should not have to spend endless hours on phone calling the credit bureau back and forth attempting to resolve the issues on my credit report! I am filing this complaint because Experian will not respond to any letters that I am sending in. I 'm sure this could be a violation of my rights and Experian should not be deliberately disregarding my concerns! \n1030-1.0100.02978800.011795000.289031000.058887000.015777000.009849001000010009
24Credit reportingI am requesting that EXPERIAN immediately delete the address listed on my credit file as reported and previously listed on the EXPERIAN Credit File as : XXXX XXXX XXXX XXXX XXXX WA XXXX. Address indentification number : XXXX, Geographical code : XXXX \n1030-1.0100.12501900.010463000.289031000.058887000.022723000.004580001000000108
76Credit reportingWhile checking my personal credit report, I noticed an unauthorized and fraudulent credit inquiry made by XXXX on or about XX/XX/XXXX on Experian. I did not authorized anyone employed by this company to make any inquiry and view my credit report. XXXX has violated the Fair Credit Reporting Act Section 1681b ( c ). They were not legally entitled to make this fraudulent inquiry. This is a serious breach of my privacy rights. \nI have requested that they mail me a copy of my signed authorization form that gave them the right to view my credit within five ( 5 ) business days so that I can verify its validity and advised them that if they can not provide me with proof that I authorized them to view my credit report then I am demanding that they contact the credit bureaus immediately and have them remove the unauthorized and fraudulent hard inquiry immediately. I also requested that they remove my personal information from their records. My Social Security # is XXXX and my Date of Birth is XX/XX/XXXX in case it is needed to locate the fraudulent inquiry in their system. \n1032-1.0100.01029800.009565010.289031000.058887000.149433000.007484000001000108
49Credit reportingThis is a formal complaint against TRANSUNIONAccording to my credit report, XXXX XXXX is currently reporting to TRANSUNION that I applied for credit with XXXX XXXX on XXXX/XXXX/2015 I did not grant TRANSUNION authorization to provide access to my credit report to XXXX XXXX ; or share my credit report with XXXX XXXX. \n\nThe Fair Credit Reporting Act requires that a creditor be able to verify the written authorization of the consumer giving the creditor permission to review their credit. If you can provide a copy of a credit application authorizing the disclosure of my credit files with my signature, I will accept the inquiry. If a signed authorization can not be found please remove the inquiry. \n\nThe presence of this inquiry is adversely affecting my credit report. Time is of the essence so I would greatly appreciate a response from you immediately. Please mail me the copy of a signed application or a letter indicating your intention to delete the inquiry. \n1130-1.0100.01029800.009565010.289031000.057824000.022723000.004580001000010007
50Credit reportingThis is a formal complaint against TRANSUNIONAccording to my credit report, XXXX is currently reporting to TRANSUNION that I applied for credit with XXXX on XXXX/XXXX/2014 I did not grant TRANSUNION authorization to provide access to my credit report to XXXX ; or share my credit report with XXXX. \n\nThe Fair Credit Reporting Act requires that a creditor be able to verify the written authorization of the consumer giving the creditor permission to review their credit. If you can provide a copy of a credit application authorizing the disclosure of my credit files with my signature, I will accept the inquiry. If a signed authorization can not be found please remove the inquiry. \n\nThe presence of this inquiry is adversely affecting my credit report. Time is of the essence so I would greatly appreciate a response from you immediately. Please mail me the copy of a signed application or a letter indicating your intention to delete the inquiry. \n1130-1.0100.01029800.009565010.289031000.057824000.022723000.004580001000010007
43Credit reportingIn XXXX, I requested my free annual credit report. After viewing my credit report, I noticed over XXXX credit/loan inquiries on my account that was not authorized by me. I contacted each company and was told that there was not any account open under my information. I explained that I have a hard inquiry on my credit report and need it removed as soon as possible so that I could apply for a home. I was told that the inquiries would be removed from my credit report. As of XXXX/XXXX/XXXX, none of the inquiries have been removed dating back to XXXX. I had a initial fraud alert placed on my report on XXXX XXXX, so if any credit reports are being pulled with my information, I was to be contacted by the company before any action was taken place. I just want the inquiries to be removed so that my scores and credit is not being impacted any longer. I would like to purchase a house, but until all of the inquires are removed I will not be approved with XXXX inquires on my credit report. \n1033-1.0100.12501900.03981700-1.000000100.062794000.046568000.000763011000010006
8Credit reportingA few months ago I ended up filing some disputes because there was incorrect and incomplete information on my credit report. So I prepared a nice letter and sent it with a copy of my driver 's license, proof of my social security number and proof of my mailing address so that Experian would be able to verify my identity. \nIt has been at least 2 months since I mailed my letter and I have n't heard back. When I try calling their XXXX number they want a report number before I speak with an agent and since I do n't have XXXX I ca n't speak to anyone. \nI thought that the credit companies had to respond back to me within 30 days otherwise they are supposed to remove things from my credit report?! \nWell it 's been way more than 30 days and all of the stuff I disputed is still there. I know I can go online and buy a credit monitoring service and try to dispute things online but I thought that Experian is supposed to help me for free! \nI 'm at the point where I 'm done wasting time and need to get this fixed immediately. \n1030-1.0100.02978800.011795000.289031000.058887000.015777000.009849001000010005
12Credit reportingA few months ago I requested my credit report from Experian. I got back this letter from them telling me that they received something that looked like fraud. I did n't get a chance to call them right away and I honestly thought it was some kind of mistake so I just went online and got my report from some website instead. \nI ended up filing some disputes because there was incorrect and incomplete information on my credit report. So I prepared a nice letter and sent it with a copy of my driver 's license, proof of my social security number and proof of my mailing address so that Experian would be able to verify my identity. \nIt has been at least XXXX months since I mailed my letter and I have n't heard back. When I try calling their XXXX number they want a report number before I speak with an agent and since I do n't have XXXX I ca n't speak to anyone. \nI thought that the credit companies had to respond back to me within XXXX days otherwise they are supposed to remove things from my credit report?! \nWell it 's been way more than XXXX days and all of the stuff I disputed is still there. I know I can go online and buy a credit monitoring service and try to dispute things online but I thought that Experian is supposed to help me for free! \nI 'm at the point where I 'm done wasting time and need to get this fixed immediately. \n1030-1.0100.02978800.011795000.289031000.058887000.015777000.009849001000010005